Deep learning for enhanced spectral analysis of MA-XRF datasets of paintings.

ABSTRACT: Recent advancements of noninvasive imaging techniques applied for the study and conservation of paintings have driven a rapid development of cutting-edge computational methods. Macro x-ray fluorescence (MA-XRF), a well-established tool in this domain, generates complex and voluminous datasets that pose analytical challenges. To address this, we have incorporated machine learning strategies specifically designed for the analysis as they allow for identification of nontrivial dependencies and classification within these high-dimensional data, thereby promising comprehensive interrogation. We introduce a deep learning algorithm trained on a synthetic dataset that allows for fast and accurate analysis of the XRF spectra in MA-XRF datasets. This approach successfully overcomes the limitations c

SUBMITTER: Preisler Z 

PROVIDER: S-EPMC11423876 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature